code: def is_numerical_column(
      data: pd.Series,
      valid_data: Optional[pd.Series] = None,
  ) -> bool:
      """
      Identify if a column is a numerical column.
      Here it uses a very simple rule to verify if this is a numerical column.
  
      Parameters
      ----------
      data
          One column of a multimodal pd.DataFrame for training.
      valid_data
          One column of a multimodal pd.DataFrame for validation.
  
      Returns
      -------
      Whether the column is a numerical column.
      """
      try:
          numerical_data = pd.to_numeric(data)
          if valid_data is not None:
              numerical_valid_data = pd.to_numeric(valid_data)
          return True
      except:
          return False
  
imports: import collections
  import json
  import logging
  import warnings
  from io import BytesIO
  from typing import Dict, List, Optional, Tuple, Union
  import pandas
  import PIL
  import pytesseract
  from autogluon.core.utils import infer_problem_type
  from ..constants import BINARY, CATEGORICAL, CLASSIFICATION, DOCUMENT_IMAGE, DOCUMENT_PDF, IDENTIFIER, IMAGE, IMAGE_BYTEARRAY, IMAGE_PATH, MULTICLASS, NER, NER_ANNOTATION, NULL, NUMERICAL, OBJECT_DETECTION, OPEN_VOCABULARY_OBJECT_DETECTION, REGRESSION, ROIS, SEMANTIC_SEGMENTATION, SEMANTIC_SEGMENTATION_GT, SEMANTIC_SEGMENTATION_IMG, TEXT, TEXT_NER
  from .utils import is_rois_input
label_desc: Use image node_id 3 for calling a global function with example usage: is_numerical_column(data, valid_data) and returns: True, False